Articles | Volume 19, issue 6
https://doi.org/10.5194/gmd-19-2385-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-2385-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The spatio-temporal visualization tool HMMLVis in renewable energy applications
Rainer Wöß
CORRESPONDING AUTHOR
Research Group Data Mining and Machine Learning, Faculty of Computer Science, University of Vienna, Währingerstrasse 29, 1090 Vienna, Austria
Kateřina Hlaváčková-Schindler
CORRESPONDING AUTHOR
Research Group Data Mining and Machine Learning, Faculty of Computer Science, University of Vienna, Währingerstrasse 29, 1090 Vienna, Austria
Data Science @ Uni Vienna, University of Vienna, Vienna, Austria
Irene Schicker
CORRESPONDING AUTHOR
GeoSphere Austria, Hohe Warte 38, 1190 Vienna, Austria
Petrina Papazek
CORRESPONDING AUTHOR
GeoSphere Austria, Hohe Warte 38, 1190 Vienna, Austria
Claudia Plant
CORRESPONDING AUTHOR
Research Group Data Mining and Machine Learning, Faculty of Computer Science, University of Vienna, Währingerstrasse 29, 1090 Vienna, Austria
Data Science @ Uni Vienna, University of Vienna, Vienna, Austria
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Daan R. Scheepens, Irene Schicker, Kateřina Hlaváčková-Schindler, and Claudia Plant
Geosci. Model Dev., 16, 251–270, https://doi.org/10.5194/gmd-16-251-2023, https://doi.org/10.5194/gmd-16-251-2023, 2023
Short summary
Short summary
The production of wind energy is increasing rapidly and relies heavily on atmospheric conditions. To ensure power grid stability, accurate predictions of wind speed are needed, especially in the short range and for extreme wind speed ranges. In this work, we demonstrate the forecasting skills of a data-driven deep learning model with model adaptations to suit higher wind speed ranges. The resulting model can be applied to other data and parameters, too, to improve nowcasting predictions.
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Short summary
Our tool is an easy-to-use, interpretable causal inference software. It can be applied in any scientific discipline exploring time series. The tool uses heterogeneous Granger causality. It can be used on time-series data to infer causal relationships between multiple variables and a target time-series. The tool is demonstrated on different types of applications related to meteorological events in a renewable energy, air pollution, and postprocessing benchmark data.
Our tool is an easy-to-use, interpretable causal inference software. It can be applied in any...
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